library(Seurat)
library(DAAG)
library(tidyverse)
library(relaimpo)
library(bootstrap)
Read in tumor object
subset tumor seurat obeject to TN only
tn_samples <- filter(tiss_subset_tumor2@meta.data, sample_name == "LT_S34" | sample_name == "LT_S43" | sample_name == "LT_S45" | sample_name == "LT_S49" | sample_name == "LT_S52" | sample_name == "LT_S51" | sample_name == "LT_S56" | sample_name == "LT_S67" | sample_name == "LT_S69" | sample_name == "LT_S74" | sample_name == "LT_S75")
tn_seurat <- SubsetData(tiss_subset_tumor2, cells.use = tn_samples$cell_id)
rownames(tn_seurat@meta.data) <- tn_seurat@meta.data$cell_id
Investigate each Signature found from grouped analysis: 1. Alveolar Sig 2. Kynurenine Sig 3. Plasminogen Sig 4. Serpine1 5. Gap Junction Sig
- Alveolar Sig
DOR_Alveolar <- as.data.frame(FetchData(object = tn_seurat, vars.all = c("SFTPC", "SFTPB", "SFTPD", "PGC", "CLDN18", "AQP4", "SCGB3A1", "ABCA3", "GATA6", "NKX2-1", "SFTA3", "IGFBP2", "HOPX", "NAPSA", "FOXA2", "AGER", "LAMP1")))
DOR_Alveolar$cell_id <- rownames(DOR_Alveolar)
DOR_Alveolar <- merge(tn_seurat@meta.data, DOR_Alveolar, by = "cell_id")
rownames(DOR_Alveolar) <- DOR_Alveolar$cell_id
- Kynurenine Sig
DOR_Kynurenine <- as.data.frame(FetchData(object = tn_seurat, vars.all = c('IDO1', 'KYNU', 'QPRT')))
DOR_Kynurenine$cell_id <- rownames(DOR_Kynurenine)
DOR_Kynurenine <- merge(tn_seurat@meta.data, DOR_Kynurenine, by = "cell_id")
rownames(DOR_Kynurenine) <- DOR_Kynurenine$cell_id
- Plasminogen Sig
DOR_Plasminogen <- as.data.frame(FetchData(object = tn_seurat, vars.all = c('ANXA2', 'PLAT', 'PLAU', 'PLAUR')))
DOR_Plasminogen$cell_id <- rownames(DOR_Plasminogen)
DOR_Plasminogen <- merge(tn_seurat@meta.data, DOR_Plasminogen, by = "cell_id")
rownames(DOR_Plasminogen) <- DOR_Plasminogen$cell_id
- Serpine1
DOR_SERPINE1 <- as.data.frame(FetchData(object = tn_seurat, vars.all = c('SERPINE1')))
DOR_SERPINE1$cell_id <- rownames(DOR_SERPINE1)
DOR_SERPINE1 <- merge(tn_seurat@meta.data, DOR_SERPINE1, by = "cell_id")
rownames(DOR_SERPINE1) <- DOR_SERPINE1$cell_id
- Gap Junction Sig
DOR_GapJunction <- as.data.frame(FetchData(object = tn_seurat, vars.all = c('GJB3', 'GJB2', 'GJB4','GJB5')))
DOR_GapJunction$cell_id <- rownames(DOR_GapJunction)
DOR_GapJunction <- merge(tn_seurat@meta.data, DOR_GapJunction, by = "cell_id")
rownames(DOR_GapJunction) <- DOR_GapJunction$cell_id
fit 1 = Alveolar Sig
summary(fit1) # show results
Call:
lm(formula = dor ~ SFTPC + SFTPB + SFTPD + PGC + CLDN18 + AQP4 +
SCGB3A1 + ABCA3 + GATA6 + `NKX2-1` + SFTA3 + IGFBP2 + HOPX +
NAPSA + FOXA2 + AGER + LAMP1, data = DOR_Alveolar)
Residuals:
Min 1Q Median 3Q Max
-0.31790 -0.03572 0.00897 0.04640 0.19908
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.6616254 0.0056816 116.451 < 2e-16 ***
SFTPC 0.0116742 0.0063559 1.837 0.066646 .
SFTPB 0.0022941 0.0025315 0.906 0.365098
SFTPD -0.0429224 0.0107056 -4.009 6.7e-05 ***
PGC -0.0082526 0.0135026 -0.611 0.541261
CLDN18 0.0007957 0.0196329 0.041 0.967680
AQP4 -0.0040422 0.0107509 -0.376 0.707031
SCGB3A1 -0.0011107 0.0060576 -0.183 0.854568
ABCA3 -0.0034290 0.0096255 -0.356 0.721763
GATA6 -0.0226240 0.0228514 -0.990 0.322471
`NKX2-1` -0.0451323 0.0044903 -10.051 < 2e-16 ***
SFTA3 0.0054035 0.0059639 0.906 0.365205
IGFBP2 0.0330716 0.0016918 19.548 < 2e-16 ***
HOPX -0.0335134 0.0036519 -9.177 < 2e-16 ***
NAPSA -0.0302077 0.0033578 -8.996 < 2e-16 ***
FOXA2 -0.0273386 0.0044887 -6.091 1.8e-09 ***
AGER -0.0907966 0.0243912 -3.723 0.000212 ***
LAMP1 0.0030322 0.0095368 0.318 0.750610
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.071 on 745 degrees of freedom
Multiple R-squared: 0.7277, Adjusted R-squared: 0.7214
F-statistic: 117.1 on 17 and 745 DF, p-value: < 2.2e-16
fit2 = Kynurenine Sig
fit2 <- lm(dor ~ IDO1 + KYNU + QPRT, data=DOR_Kynurenine)
summary(fit2) # show results
Call:
lm(formula = dor ~ IDO1 + KYNU + QPRT, data = DOR_Kynurenine)
Residuals:
Min 1Q Median 3Q Max
-0.35619 -0.09619 -0.09157 0.15381 0.25432
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.656187 0.005075 129.289 < 2e-16 ***
IDO1 -0.025909 0.013803 -1.877 0.06089 .
KYNU 0.026314 0.009387 2.803 0.00519 **
QPRT -0.103465 0.015602 -6.631 6.31e-11 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.1303 on 759 degrees of freedom
Multiple R-squared: 0.0659, Adjusted R-squared: 0.0622
F-statistic: 17.85 on 3 and 759 DF, p-value: 3.348e-11
# diagnostic plots
plot(fit2)




ggplot(DOR_Kynurenine, aes(x = IDO1, y = dor, color = sample_name)) + geom_point()

ggplot(DOR_Kynurenine, aes(x = KYNU, y = dor, color = sample_name)) + geom_point()

ggplot(DOR_Kynurenine, aes(x = QPRT, y = dor, color = sample_name)) + geom_point()

fit3 = Plasminogen Sig
fit3 <- lm(dor ~ PLAU + PLAUR + PLAT + ANXA2, data=DOR_Plasminogen)
summary(fit3) # show results
Call:
lm(formula = dor ~ PLAU + PLAUR + PLAT + ANXA2, data = DOR_Plasminogen)
Residuals:
Min 1Q Median 3Q Max
-0.39284 -0.07108 0.02876 0.07998 0.24803
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.515175 0.013690 37.631 < 2e-16 ***
PLAU -0.026385 0.004716 -5.595 3.08e-08 ***
PLAUR -0.031850 0.006093 -5.227 2.22e-07 ***
PLAT -0.043839 0.003904 -11.229 < 2e-16 ***
ANXA2 0.056471 0.003923 14.393 < 2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.1065 on 758 degrees of freedom
Multiple R-squared: 0.3766, Adjusted R-squared: 0.3733
F-statistic: 114.5 on 4 and 758 DF, p-value: < 2.2e-16
# diagnostic plots
plot(fit3)




ggplot(DOR_Plasminogen, aes(x = PLAU, y = dor, color = sample_name)) + geom_point()

ggplot(DOR_Plasminogen, aes(x = PLAUR, y = dor, color = sample_name)) + geom_point()

ggplot(DOR_Plasminogen, aes(x = PLAT, y = dor, color = sample_name)) + geom_point()

ggplot(DOR_Plasminogen, aes(x = ANXA2, y = dor, color = sample_name)) + geom_point()

fit4 = SERPINE1
fit4 <- lm(dor ~ SERPINE1, data=DOR_SERPINE1)
summary(fit4) # show results
Call:
lm(formula = dor ~ SERPINE1, data = DOR_SERPINE1)
Residuals:
Min 1Q Median 3Q Max
-0.36405 -0.10405 -0.04199 0.14595 0.25223
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.664046 0.005113 129.880 < 2e-16 ***
SERPINE1 -0.047593 0.007384 -6.446 2.04e-10 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.1311 on 761 degrees of freedom
Multiple R-squared: 0.05177, Adjusted R-squared: 0.05052
F-statistic: 41.55 on 1 and 761 DF, p-value: 2.042e-10
# diagnostic plots
plot(fit4)




ggplot(DOR_SERPINE1, aes(x = SERPINE1, y = dor, color = sample_name)) + geom_point()

fit5 = Gap Junction Sig
fit5 <- lm(dor ~ GJB3 + GJB2 + GJB4 + GJB5, data=DOR_GapJunction)
summary(fit5) # show results
Call:
lm(formula = dor ~ GJB3 + GJB2 + GJB4 + GJB5, data = DOR_GapJunction)
Residuals:
Min 1Q Median 3Q Max
-0.33107 -0.07003 -0.07003 0.14248 0.17997
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.630034 0.004815 130.836 < 2e-16 ***
GJB3 0.029685 0.018856 1.574 0.1158
GJB2 0.042758 0.023258 1.838 0.0664 .
GJB4 0.102237 0.016175 6.321 4.44e-10 ***
GJB5 0.124777 0.014473 8.622 < 2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.1227 on 758 degrees of freedom
Multiple R-squared: 0.1722, Adjusted R-squared: 0.1678
F-statistic: 39.42 on 4 and 758 DF, p-value: < 2.2e-16
# diagnostic plots
plot(fit5)




ggplot(DOR_GapJunction, aes(x = GJB2, y = dor, color = sample_name)) + geom_point()

ggplot(DOR_GapJunction, aes(x = GJB3, y = dor, color = sample_name)) + geom_point()

ggplot(DOR_GapJunction, aes(x = GJB4, y = dor, color = sample_name)) + geom_point()

ggplot(DOR_GapJunction, aes(x = GJB5, y = dor, color = sample_name)) + geom_point()

# # K-fold cross-validation
# cv.lm(data = DOR_GapJunction, form.lm = fit5, m = 10, plotit = FALSE)
# # Assessing R2 shrinkage using 10-Fold Cross-Validation
# # define functions
# theta.fit <- function(x,y){lsfit(x,y)}
# theta.predict <- function(fit5,x){cbind(1,x)%*%fit5$coef}
#
# # matrix of predictors
# X <- as.matrix(DOR_GapJunction[c("GJB3","GJB2","GJB4","GJB5")])
# # vector of predicted values
# y <- as.matrix(DOR_GapJunction[c("dor")])
#
# results <- crossval(X,y,theta.fit,theta.predict,ngroup=10)
# cor(y, fit5$fitted.values)**2 # raw R2
# cor(y,results$cv.fit5)**2 # cross-validated R2
#
# # Calculate Relative Importance for Each Predictor
# calc.relimp(fit5,type = c("lmg","last","first","pratt"), rela=TRUE)
# # Bootstrap Measures of Relative Importance (1000 samples)
# boot <- boot.relimp(fit5, b = 1000, type = c("lmg", "last", "first", "pratt"), rank = TRUE, diff = TRUE, rela = TRUE)
# booteval.relimp(boot) # print result
# plot(booteval.relimp(boot,sort=TRUE)) # plot result
table(tn_seurat@meta.data$biopsy_site, tn_seurat@meta.data$dor)
0.3 0.31 0.43 0.46 0.5 0.56 0.57 0.7 0.81
Adrenal 0 1 0 0 0 0 0 0 0
Brain 0 0 0 0 0 0 0 0 0
Liver 0 0 0 0 0 0 0 28 0
LN 0 0 5 0 6 305 16 0 0
Lung 14 0 0 0 0 71 0 0 293
Pleura 0 0 0 24 0 0 0 0 0
table(tn_seurat@meta.data$sample_name, tn_seurat@meta.data$dor)
0.3 0.31 0.43 0.46 0.5 0.56 0.57 0.7 0.81
LT_S34 0 0 0 24 0 0 0 0 0
LT_S43 0 0 5 0 0 0 0 0 0
LT_S45 0 1 0 0 0 0 0 0 0
LT_S49 0 0 0 0 6 0 0 0 0
LT_S51 0 0 0 0 0 0 16 0 0
LT_S52 14 0 0 0 0 0 0 0 0
LT_S56 0 0 0 0 0 0 0 0 291
LT_S67 0 0 0 0 0 0 0 0 2
LT_S69 0 0 0 0 0 305 0 0 0
LT_S74 0 0 0 0 0 71 0 0 0
LT_S75 0 0 0 0 0 0 0 28 0
table(tn_seurat@meta.data$sample_name)
LT_S34 LT_S43 LT_S45 LT_S49 LT_S51 LT_S52 LT_S56 LT_S67 LT_S69 LT_S74 LT_S75
24 5 1 6 16 14 291 2 305 71 28
Bulkize the samples
tn_seurat <- SetIdent(tn_seurat, ident.use = tn_seurat@meta.data$sample_name)
table(tn_seurat@ident)
LT_S34 LT_S43 LT_S45 LT_S49 LT_S51 LT_S52 LT_S56 LT_S67 LT_S69 LT_S74 LT_S75
24 5 1 6 16 14 291 2 305 71 28
sample.averages <- AverageExpression(object = tn_seurat)
Finished averaging RNA for cluster LT_S34
Finished averaging RNA for cluster LT_S43
Finished averaging RNA for cluster LT_S45
Finished averaging RNA for cluster LT_S49
Finished averaging RNA for cluster LT_S51
Finished averaging RNA for cluster LT_S52
Finished averaging RNA for cluster LT_S56
Finished averaging RNA for cluster LT_S67
Finished averaging RNA for cluster LT_S69
Finished averaging RNA for cluster LT_S74
Finished averaging RNA for cluster LT_S75
To find DE genes between bulkized TN samples with low and high DOR, export table with groups
# set up table
sample.averages.t <- as.data.frame(t(sample.averages))
head(sample.averages.t)
sample.averages.t$sample_name <- rownames(sample.averages.t)
sample.averages.t <- left_join(sample.averages.t, dor_meta, by = "sample_name")
rownames(sample.averages.t) <- sample.averages.t$sample_name
length(colnames(sample.averages.t))
[1] 26489
DE_avg <- pairwise.wilcox.test(x = sample.averages.t$EGFR, g = sample.averages.t$dor_class)
write.csv(sample.averages.t, file = "/myVolume/TN_bulkized_data.csv")
TN.sample.averages <- sample.averages
head(TN.sample.averages)
Bulkize fit analysis Alveolar

Bulkize fit analysis Kynurenine

Bulkize fit analysis Plasminogen

Bulkize fit analysis of SERPINE1

Bulkize fit analysis GapJunction

bulkized_TN_markers <- read.csv(file = paste(dir, "Data_input/mwu_luad.csv", sep = ""))
bulkized_TN_markers.f <- filter(bulkized_TN_markers, pval_1 <= 0.05)
hist(bulkized_TN_markers.f$stat_1)

length(bulkized_TN_markers.f$pval_1)
[1] 4115
bulkized_TN_markers.f <- bulkized_TN_markers.f[order(bulkized_TN_markers.f$stat_1, decreasing = FALSE), ]
Most compelling high expression corr to low dor
ggplot(sample.averages.t, aes(x = ADAR, y = dor)) + geom_point(aes(color = patient_id))

ggplot(sample.averages.t, aes(x = CFL1, y = dor)) + geom_point(aes(color = patient_id))

Most compelling high expression corr to high dor
ggplot(sample.averages.t, aes(x = TTLL13P, y = dor)) + geom_point(aes(color = dor_class))

ggplot(sample.averages.t, aes(x = ALS2, y = dor)) + geom_point(aes(color = dor_class))

ggplot(sample.averages.t, aes(x = RLN1, y = dor)) + geom_point(aes(color = dor_class))

ggplot(sample.averages.t, aes(x = USP45, y = dor)) + geom_point(aes(color = dor_class))

ggplot(sample.averages.t, aes(x = BDKRB1, y = dor)) + geom_point(aes(color = dor_class))

ggplot(sample.averages.t, aes(x = LINC01061, y = dor)) + geom_point(aes(color = dor_class))

ggplot(sample.averages.t, aes(x = ZNF563, y = dor)) + geom_point(aes(color = dor_class))

ggplot(sample.averages.t, aes(x = WDR19, y = dor)) + geom_point(aes(color = dor_class))

---
title: "Regression of Clinical Outcomes to Sigs"
output: html_notebook
---

```{r}
library(Seurat)
library(DAAG)
library(tidyverse)
library(relaimpo)
library(bootstrap)
```

Read in tumor object
```{r}
# rm(list=ls())
dir <- "/myVolume/scell_lung_adenocarcinoma/"
load(file = paste(dir, "Data_input/objects/NI04_tumor_seurat_object.RData", sep = ""))

#Read in depth of response clinical outcomes
dor_meta <- read.csv(file = paste(dir, "Data_input/csv_files/depthofresponse_tn.csv", sep = ""))
#correct misannoation in dor
dor_meta$sample_name <- gsub(pattern = "LT_S57", replacement = "LT_S51", x = dor_meta$sample_name)
dor_meta$dor <- gsub(pattern = ".12", replacement = ".46", x = dor_meta$dor)
dor_meta$dor_class <- c("low", "low", "low", "low", "low", "high", "high", "high", "high", "high", "high")
dor_meta
```

subset tumor seurat obeject to TN only
```{r}
tn_samples <- filter(tiss_subset_tumor2@meta.data, sample_name == "LT_S34" | sample_name == "LT_S43" | sample_name == "LT_S45" | sample_name == "LT_S49" | sample_name == "LT_S52" | sample_name == "LT_S51" | sample_name == "LT_S56" | sample_name == "LT_S67" | sample_name == "LT_S69" | sample_name == "LT_S74" | sample_name == "LT_S75")

tn_seurat <- SubsetData(tiss_subset_tumor2, cells.use = tn_samples$cell_id)
rownames(tn_seurat@meta.data) <- tn_seurat@meta.data$cell_id
```


```{r}
tn_seurat@meta.data <- merge(dor_meta[,c(2:4)], tn_seurat@meta.data, by = "sample_name")
rownames(tn_seurat@meta.data) <- tn_seurat@meta.data$cell_id
```


Investigate each Signature found from grouped analysis:
1. Alveolar Sig
2. Kynurenine Sig
3. Plasminogen Sig
4. Serpine1
5. Gap Junction Sig

1. Alveolar Sig
```{r}
DOR_Alveolar <- as.data.frame(FetchData(object = tn_seurat, vars.all = c("SFTPC", "SFTPB", "SFTPD", "PGC", "CLDN18", "AQP4", "SCGB3A1", "ABCA3", "GATA6", "NKX2-1", "SFTA3", "IGFBP2", "HOPX", "NAPSA", "FOXA2", "AGER", "LAMP1")))
DOR_Alveolar$cell_id <- rownames(DOR_Alveolar)
DOR_Alveolar <- merge(tn_seurat@meta.data, DOR_Alveolar, by = "cell_id")
rownames(DOR_Alveolar) <- DOR_Alveolar$cell_id
```

2. Kynurenine Sig
```{r}
DOR_Kynurenine <- as.data.frame(FetchData(object = tn_seurat, vars.all = c('IDO1', 'KYNU', 'QPRT')))
DOR_Kynurenine$cell_id <- rownames(DOR_Kynurenine)
DOR_Kynurenine <- merge(tn_seurat@meta.data, DOR_Kynurenine, by = "cell_id")
rownames(DOR_Kynurenine) <- DOR_Kynurenine$cell_id
```

3. Plasminogen Sig
```{r}
DOR_Plasminogen <- as.data.frame(FetchData(object = tn_seurat, vars.all = c('ANXA2', 'PLAT', 'PLAU', 'PLAUR')))
DOR_Plasminogen$cell_id <- rownames(DOR_Plasminogen)
DOR_Plasminogen <- merge(tn_seurat@meta.data, DOR_Plasminogen, by = "cell_id")
rownames(DOR_Plasminogen) <- DOR_Plasminogen$cell_id
```

4. Serpine1
```{r}
DOR_SERPINE1 <- as.data.frame(FetchData(object = tn_seurat, vars.all = c('SERPINE1')))
DOR_SERPINE1$cell_id <- rownames(DOR_SERPINE1)
DOR_SERPINE1 <- merge(tn_seurat@meta.data, DOR_SERPINE1, by = "cell_id")
rownames(DOR_SERPINE1) <- DOR_SERPINE1$cell_id
```

5. Gap Junction Sig
```{r}
DOR_GapJunction <- as.data.frame(FetchData(object = tn_seurat, vars.all = c('GJB3', 'GJB2', 'GJB4','GJB5')))
DOR_GapJunction$cell_id <- rownames(DOR_GapJunction)
DOR_GapJunction <- merge(tn_seurat@meta.data, DOR_GapJunction, by = "cell_id")
rownames(DOR_GapJunction) <- DOR_GapJunction$cell_id
```

fit 1 = Alveolar Sig
```{r}
fit1 <- lm(dor ~ SFTPC +SFTPB + SFTPD + PGC + CLDN18 + AQP4 + SCGB3A1 + ABCA3 + GATA6 + `NKX2-1` + SFTA3 + IGFBP2+ HOPX + NAPSA + FOXA2 + AGER + LAMP1, data=DOR_Alveolar)
summary(fit1) # show results

# diagnostic plots
plot(fit1)

ggplot(DOR_Alveolar, aes(x = `NKX2-1`, y = dor, color = sample_name)) + geom_point()
ggplot(DOR_Alveolar, aes(x = IGFBP2, y = dor, color = sample_name)) + geom_point()
ggplot(DOR_Alveolar, aes(x = HOPX, y = dor, color = sample_name)) + geom_point()
ggplot(DOR_Alveolar, aes(x = NAPSA, y = dor, color = sample_name)) + geom_point()
ggplot(DOR_Alveolar, aes(x = FOXA2, y = dor, color = sample_name)) + geom_point()
```

fit2 = Kynurenine Sig
```{r}
fit2 <- lm(dor ~ IDO1 + KYNU + QPRT, data=DOR_Kynurenine)
summary(fit2) # show results

# diagnostic plots 
plot(fit2)

ggplot(DOR_Kynurenine, aes(x = IDO1, y = dor, color = sample_name)) + geom_point()
ggplot(DOR_Kynurenine, aes(x = KYNU, y = dor, color = sample_name)) + geom_point()
ggplot(DOR_Kynurenine, aes(x = QPRT, y = dor, color = sample_name)) + geom_point()
```

fit3 = Plasminogen Sig
```{r}
fit3 <- lm(dor ~ PLAU + PLAUR + PLAT + ANXA2, data=DOR_Plasminogen)
summary(fit3) # show results

# diagnostic plots 
plot(fit3)

ggplot(DOR_Plasminogen, aes(x = PLAU, y = dor, color = sample_name)) + geom_point()
ggplot(DOR_Plasminogen, aes(x = PLAUR, y = dor, color = sample_name)) + geom_point()
ggplot(DOR_Plasminogen, aes(x = PLAT, y = dor, color = sample_name)) + geom_point()
ggplot(DOR_Plasminogen, aes(x = ANXA2, y = dor, color = sample_name)) + geom_point()
```

fit4 = SERPINE1
```{r}
fit4 <- lm(dor ~ SERPINE1, data=DOR_SERPINE1)
summary(fit4) # show results

# diagnostic plots 
plot(fit4)

ggplot(DOR_SERPINE1, aes(x = SERPINE1, y = dor, color = sample_name)) + geom_point()
```

fit5 = Gap Junction Sig
```{r}
fit5 <- lm(dor ~ GJB3 + GJB2 + GJB4 + GJB5, data=DOR_GapJunction)
summary(fit5) # show results

# diagnostic plots 
plot(fit5)

ggplot(DOR_GapJunction, aes(x = GJB2, y = dor, color = sample_name)) + geom_point()
ggplot(DOR_GapJunction, aes(x = GJB3, y = dor, color = sample_name)) + geom_point()
ggplot(DOR_GapJunction, aes(x = GJB4, y = dor, color = sample_name)) + geom_point()
ggplot(DOR_GapJunction, aes(x = GJB5, y = dor, color = sample_name)) + geom_point()

# # K-fold cross-validation
# cv.lm(data = DOR_GapJunction, form.lm = fit5, m = 10, plotit = FALSE)
# # Assessing R2 shrinkage using 10-Fold Cross-Validation 
# # define functions 
# theta.fit <- function(x,y){lsfit(x,y)}
# theta.predict <- function(fit5,x){cbind(1,x)%*%fit5$coef} 
# 
# # matrix of predictors
# X <- as.matrix(DOR_GapJunction[c("GJB3","GJB2","GJB4","GJB5")])
# # vector of predicted values
# y <- as.matrix(DOR_GapJunction[c("dor")]) 
# 
# results <- crossval(X,y,theta.fit,theta.predict,ngroup=10)
# cor(y, fit5$fitted.values)**2 # raw R2 
# cor(y,results$cv.fit5)**2 # cross-validated R2
# 
# # Calculate Relative Importance for Each Predictor
# calc.relimp(fit5,type = c("lmg","last","first","pratt"), rela=TRUE)
# # Bootstrap Measures of Relative Importance (1000 samples) 
# boot <- boot.relimp(fit5, b = 1000, type = c("lmg", "last", "first", "pratt"), rank = TRUE, diff = TRUE, rela = TRUE)
# booteval.relimp(boot) # print result
# plot(booteval.relimp(boot,sort=TRUE)) # plot result
```

```{r}
table(tn_seurat@meta.data$biopsy_site, tn_seurat@meta.data$dor)
table(tn_seurat@meta.data$sample_name, tn_seurat@meta.data$dor)
table(tn_seurat@meta.data$sample_name)
```

Bulkize the samples
```{r}
tn_seurat <- SetIdent(tn_seurat, ident.use = tn_seurat@meta.data$sample_name)
table(tn_seurat@ident)
sample.averages <- AverageExpression(object = tn_seurat)
```

To find DE genes between bulkized TN samples with low and high DOR, export table with groups
```{r}
# set up table 
sample.averages.t <- as.data.frame(t(sample.averages))
head(sample.averages.t)
sample.averages.t$sample_name <- rownames(sample.averages.t)
sample.averages.t <- left_join(sample.averages.t, dor_meta, by = "sample_name")
rownames(sample.averages.t) <- sample.averages.t$sample_name

length(colnames(sample.averages.t))
DE_avg <- pairwise.wilcox.test(x = sample.averages.t$EGFR, g = sample.averages.t$dor_class)
write.csv(sample.averages.t, file = "/myVolume/TN_bulkized_data.csv")
TN.sample.averages <- sample.averages
head(TN.sample.averages)
```


Bulkize fit analysis Alveolar
```{r}
Alveolar_sig <- c("SFTPC", "SFTPB", "SFTPD", "PGC", "CLDN18", "AQP4", "SCGB3A1", "ABCA3", "GATA6", "NKX2-1", "SFTA3", "IGFBP2", "HOPX", "NAPSA", "FOXA2", "AGER", "LAMP1")
TN_Alveolar <- TN.sample.averages[Alveolar_sig, ]
TN_Alveolar_mean <- as.data.frame(colMeans(TN_Alveolar))
TN_Alveolar_mean$sample_name <- rownames(TN_Alveolar_mean)
TN_Alveolar_mean <- left_join(TN_Alveolar_mean, dor_meta, by = "sample_name")
rownames(TN_Alveolar_mean) <- TN_Alveolar_mean$sample_name

TN_Alveolar_fit <- lm(dor ~ colMeans(TN_Alveolar), data= TN_Alveolar_mean)
summary(TN_Alveolar_fit)
TN_Alveolar_mean$predlm <- predict(TN_Alveolar_fit)

ggp_TN_Alveolar <- ggplot(TN_Alveolar_mean, aes(x = colMeans(TN_Alveolar), y = dor, color = dor_class)) + geom_point()

ggsave(ggp_TN_Alveolar, filename = paste(dir, "plot_out/NI08/TN_Alveolar_bulkized.pdf", sep = ""))
```

Bulkize fit analysis Kynurenine
```{r}
Kynurenine_sig <- c('IDO1', 'KYNU', 'QPRT')
TN_Kynurenine <- TN.sample.averages[Kynurenine_sig, ]
TN_Kynurenine_mean <- as.data.frame(colMeans(TN_Kynurenine))
TN_Kynurenine_mean$sample_name <- rownames(TN_Kynurenine_mean)
TN_Kynurenine_mean <- left_join(TN_Kynurenine_mean, dor_meta, by = "sample_name")
rownames(TN_Kynurenine_mean) <- TN_Kynurenine_mean$sample_name

TN_Kynurenine_fit <- lm(dor ~ colMeans(TN_Kynurenine), data= TN_Kynurenine_mean)
summary(TN_Kynurenine_fit)

ggp_TN_Kynurenine <- ggplot(TN_Kynurenine_mean, aes(x = colMeans(TN_Kynurenine), y = dor)) + geom_point(aes(color=dor_class))
ggsave(ggp_TN_Kynurenine, filename = paste(dir, "plot_out/NI08/TN_Kynurenine_bulkized.pdf", sep = ""))
```

Bulkize fit analysis Plasminogen
```{r}
Plasminogen_sig <- c('ANXA2', 'PLAT', 'PLAU', 'PLAUR')
TN_Plasminogen <- TN.sample.averages[Plasminogen_sig, ]
TN_Plasminogen_mean <- as.data.frame(colMeans(TN_Plasminogen))
TN_Plasminogen_mean$sample_name <- rownames(TN_Plasminogen_mean)
TN_Plasminogen_mean <- left_join(TN_Plasminogen_mean, dor_meta, by = "sample_name")
rownames(TN_Plasminogen_mean) <- TN_Plasminogen_mean$sample_name

TN_Plasminogen_fit <- lm(dor ~ colMeans(TN_Plasminogen), data= TN_Plasminogen_mean)
summary(TN_Plasminogen_fit)

ggp_TN_Plasminogen <- ggplot(TN_Plasminogen_mean, aes(x = colMeans(TN_Plasminogen), y = dor)) + geom_point(aes(color = dor_class))
ggsave(ggp_TN_Plasminogen, filename = paste(dir, "plot_out/NI08/TN_Plasminogen_bulkized.pdf", sep = ""))
```

Bulkize fit analysis of SERPINE1
```{r}
TN_Serpine_sig <-  as.data.frame(t(TN.sample.averages["SERPINE1", ]))
TN_Serpine_sig$sample_name <- rownames(TN_Serpine_sig)
TN_Serpine_sig <- left_join(TN_Serpine_sig, dor_meta, by = "sample_name")
rownames(TN_Serpine_sig) <- TN_Serpine_sig$sample_name

TN_Serpine_fit <- lm(dor ~ SERPINE1, data= TN_Serpine_sig)
summary(TN_Serpine_fit)

ggp_TN_Serpine1 <- ggplot(TN_Serpine_sig, aes(x = SERPINE1, y = dor)) + geom_point(aes(color = dor_class))
ggsave(ggp_TN_Serpine1, filename = paste(dir, "plot_out/NI08/TN_Serpine1_bulkized.pdf", sep = ""))
```

Bulkize fit analysis GapJunction
```{r}
GapJunction_sig <- c('GJB3', 'GJB2', 'GJB4','GJB5')
TN_GapJunction <- TN.sample.averages[GapJunction_sig, ]
TN_GapJunction_mean <- as.data.frame(colMeans(TN_GapJunction))
TN_GapJunction_mean$sample_name <- rownames(TN_GapJunction_mean)
TN_GapJunction_mean <- left_join(TN_GapJunction_mean, dor_meta, by = "sample_name")
rownames(TN_GapJunction_mean) <- TN_GapJunction_mean$sample_name

TN_GapJunction_fit <- lm(dor ~ colMeans(TN_GapJunction), data= TN_GapJunction_mean)
summary(TN_GapJunction_fit)

ggp_TN_GapJunction <- ggplot(TN_GapJunction_mean, aes(x = colMeans(TN_GapJunction), y = dor)) + geom_point(aes(color = dor_class))
ggsave(ggp_TN_GapJunction, filename = paste(dir, "plot_out/NI08/TN_GapJucion_bulkized.pdf", sep = ""))
```


```{r}
bulkized_TN_markers <- read.csv(file = paste(dir, "Data_input/mwu_luad.csv", sep = ""))
bulkized_TN_markers.f <- filter(bulkized_TN_markers, pval_1 <= 0.05)
hist(bulkized_TN_markers.f$stat_1)
length(bulkized_TN_markers.f$pval_1)
bulkized_TN_markers.f <- bulkized_TN_markers.f[order(bulkized_TN_markers.f$stat_1, decreasing = TRUE), ] 
```

```{r}
table(bulkized_TN_markers.f$test)
```

Most compelling high expression corr to low dor
```{r}
ggplot(sample.averages.t, aes(x = ADAR, y = dor)) + geom_point(aes(color = patient_id))
ggplot(sample.averages.t, aes(x = CFL1, y = dor)) + geom_point(aes(color = patient_id))
```

Most compelling high expression corr to high dor
```{r}
ggplot(sample.averages.t, aes(x = TTLL13P, y = dor)) + geom_point(aes(color = dor_class))
ggplot(sample.averages.t, aes(x = ALS2, y = dor)) + geom_point(aes(color = dor_class))
ggplot(sample.averages.t, aes(x = RLN1, y = dor)) + geom_point(aes(color = dor_class))
ggplot(sample.averages.t, aes(x = USP45, y = dor)) + geom_point(aes(color = dor_class))
ggplot(sample.averages.t, aes(x = BDKRB1, y = dor)) + geom_point(aes(color = dor_class))
ggplot(sample.averages.t, aes(x = LINC01061, y = dor)) + geom_point(aes(color = dor_class))
ggplot(sample.averages.t, aes(x = ZNF563, y = dor)) + geom_point(aes(color = dor_class))
ggplot(sample.averages.t, aes(x = WDR19, y = dor)) + geom_point(aes(color = dor_class))
```

